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This course complements the course on Inferential Statistics at Coursera.
The Basics of RFree
This lab is about teaching enough R to start using it for statistical analyses.How it works100 xpLittle arithmetics with R100 xpDocumenting your code100 xpVariable assignment I100 xpVariable assignment II100 xpBack to Apples and Oranges100 xpDiscover Basic Data Types100 xpWhat's that data type?50 xpCoercion: Taming your data100 xpCreate a vector I100 xpVectors II100 xpSelection by comparison I100 xpSelection by comparison II100 xpMatrices100 xpFactors100 xpDataframes: What's a data frame?100 xpInspecting dataframes100 xpConstructing a dataframe yourself100 xpIndexing and selecting columns from a dataframe100 xpLists100 xpSelecting elements from a list100 xp
Introduction to R continuedFree
This lab continues with an introduction to RMaking a start with functions: Getting help100 xpFunctions continued100 xpFunctions continued II100 xpMaking your own functions100 xpGetting your data into R100 xpReading in your own data100 xpWorking directories in R100 xpChanging working directories in R100 xpChecking files in your working directory100 xpImporting R packages100 xp
Comparing two groupsFree
The current lab treats differences between two independent groups, paired groups and proportionsRecap hypothesis testing50 xpRecap hypothesis testing (2)50 xpComparing two proportions100 xpComparing two proportions (2)100 xpComparing two proportions (3)100 xpComparing two proportions (4)100 xpComparing two proportions (5)100 xpComparing two proportions (6)50 xpComparing two means100 xpComparing two means (2)100 xpComparing two means (3)100 xpComparing two means (4)100 xpComparing two means (5)100 xpComparing two means (6)50 xpComparing two proportions for paired samples50 xpComparing two proportions for paired samples (2)100 xpComparing two proportions for paired samples (3)100 xpComparing two proportions for paired samples (4)100 xpComparing two means for paired samples50 xpComparing two means for paired samples (2)100 xpComparing two means for paired samples (3)100 xpComparing two means for paired samples (4)100 xpSimpson's paradox50 xpSimpson's paradox (2)100 xpSimpson's paradox (3)50 xp
Investigating associations between categorical variables.Contingency Table I100 xpConditional Proportions100 xpInterpretting Conditional Proportions per Row50 xpMarginal Probabilities100 xpExpected vs. Observed Probabilities50 xpExpected Frequencies100 xpResiduals100 xpChi-Square I100 xpDegrees of Freedom50 xpChi-square II100 xpCramér’s V100 xpStandardized Residuals100 xpChi-squared Goodness of Fit I50 xpChi-squared Goodness of Fit II100 xpGoodness of Fit III50 xpAssumptions50 xpFisher's Exact Test100 xp
Predicting a response variable one predictor variable.Correlation and Regression50 xpPredictor vs. Response Variable100 xpThe Regression Equation100 xpInterpretting the Regression Coefficient50 xpCalculating the Intercept100 xpFinding the Slope and Intercept Using lm()100 xpRegression in the Population50 xpPredictive Power100 xpPitfalls in Regression I50 xpPitfalls in Regression II50 xpHypotheses100 xpTesting The Regression Model I100 xpTesting the Regression Model II50 xpTesting The Regression Model III100 xpTesting Assumptions I100 xpTesting Assumptions II100 xpTesting Assumptions III100 xpTesting Assumptions IV50 xpMaking a Prediction50 xpPrediction Interval100 xpConfidence Interval100 xpExponential Regression50 xp
Predicting a response variable with more than one predictor variable.The Regression Equation50 xpThe Regression Coefficients I100 xpThe Regression Coefficients II50 xpMultiple R squared I100 xpMultiple R squared II100 xpOverall Tests I100 xpOverall Tests II50 xpIndividual Tests of Predictors I100 xpIndividual Tests of Predictors II50 xpIndividual Tests of Predictors III100 xpAssumptions I100 xpAssumptions II100 xpAssumptions III100 xpCategorical Predictors I100 xpCategorical Predictors II50 xpRegression With a Categorical Response I100 xpRegression With a Categorical Response II50 xp
Quantitative associations: ANOVAFree
This lab treats the statistical methodology of the analysis of varianceHypothesis testing and the ANOVA50 xpAnova and types of variability100 xpCalculating the between group variance100 xpCalculating within group variance100 xpCalculating the F statistic100 xpAssumptions checking analysis of variance100 xpExplaining the anova function100 xpInterpreting the output from an ANOVA50 xpAnova: Multiple comparisons50 xpAnova: Multiple comparisons (2)100 xpTwo-way Anova50 xpTwo-way Anova (2)100 xpTwo-way Anova (3)50 xpTwo-way Anova (4)100 xpTwo-way Anova (5)50 xp
This lab will treat testing methods that can be employed when the assumptions of other methods are not metNonparametric methods: Sign test50 xpNonparametric methods: sign test (2)100 xpNonparametric methods: The Wilcoxon Signed-Rank Test50 xpNonparametric methods: The Wilcoxon Signed-Rank Test (2)100 xpNonparametric methods: The Wilcoxon rank-sum test50 xpNonparametric methods: The Wilcoxon rank-sum test (2)100 xpNonparametric methods: The Kruskall-Wallis test50 xpNonparametric methods: The Kruskall-Wallis test (2)100 xpNonparametric correlation100 xpNonparametric correlation (2)100 xp
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